DIGITAL IMAGE COIN DISCRIMINATION FOR USE WITH CONSUMER-OPERATED KIOSKS AND THE LIKE
Systems and associated methods for coin discrimination are disclosed herein. Disclosed methods for discriminating coins include recognizing strings of alphanumerical characters of the coin using Optical Character Recognition (OCR). The methods can include recognizing colors and/or reflectivity of the coin using, for example, pixel thresholding algorithms. The methods can further include adding a line to an image of the coin, and measuring angles between the line and the edges on the coin. The methods can also include generating a rectangular image of the coin using, for example, a log-polar transform, generating a series of, for example, Fourier transforms from the rectangular image, and identifying spectral peak locations and intensities in the Fourier transform results. The results of the OCR, color/reflectivity recognition, angle measurement, spectral peak location, spectral peak intensity of the coin and/or other features or aspects of coins can then be compared to known values for different coins to discriminate the coins.
The present technology is generally related to the field of coin discrimination.
BACKGROUNDVarious embodiments of consumer-operated coin counting kiosks are disclosed in, for example: U.S. Pat. Nos. 5,620,079, 6,494,776, 7,520,374, 7,584,869, 7,653,599, 7,748,619, 7,815,071, and 7,865,432; and U.S. patent application Ser. Nos. 12/758,677, 12/806,531, 61/364,360, 61/409,050, and Ser. No. 13/489043; each of which is incorporated herein in its entirety by reference.
Many consumer-operated kiosks, vending machines, and other commercial sales/service/rental machines discriminate between different coin denominations based on the size, weight and/or electromagnetic properties of metal alloys in the coin. With some known technologies, a coin can be routed through an oscillating electromagnetic field that interacts with the coin. As the coin passes through the electromagnetic field, coin properties are sensed, such as changes in inductance (from which the diameter of the coin can be derived) or the quality factor related to the amount of energy dissipated (from which conductivity/metallurgy of the coin can be obtained). The results of the interaction can be collected and compared against a list of sizes and electromagnetic properties of known coins to determine the denomination of the coin. In other known technologies, a coin can be rolled along a predetermined path and the velocity of the coin or the time to reach a certain point along the path can be measured. By comparing the measured time or velocity against the corresponding values for known coins, the denomination of the coin can be determined.
However, many coins may have similar size, mass, metallurgy, and/or spectral properties. This is especially the case in markets which are proximate to multiple countries having different coin denominations. As a result, coin counting mistakes may occur due to the coin similarities, resulting in possible losses for consumer coin counting kiosk operators. Accordingly, it would be advantageous to provide robust coin discrimination systems and methods that would work reliably for coins having similar size, mass, metallurgy, and/or spectral properties.
The following disclosure describes various embodiments of systems and associated methods for discriminating coin denominations based on optical properties of the coins. In some embodiments of the present technology, a consumer-operated kiosk (e.g., a consumer coin counting machine, prepaid card dispensing/reloading machine, etc.) includes one or more digital cameras that acquire digital images of a coin when the coin enters the viewfield of the The face, back side, and lateral edge of a typical coin includes numerous optical aspects that can be detected from the images and mapped to a suitable system (e.g., polar or rectangular coordinate system having an origin at a center the coin). Some examples of the optical aspects are alphanumerical characters, embossed images or parts of the images, dots around the edge, intersecting flat areas, and/or colors/shades of the coin. In some embodiments, the locations of the optical aspects of a coin can be compared to corresponding tabulated values for known coins in a relevant market to discriminate the coin. In some embodiments, angles between selected lines on the coin image can be used to discriminate the coins. Furthermore, distances between the optical aspects of coin (e.g., tip of George Washington's nose to letter “R” in the word “TRUST”) be determined and used to discriminate the coins. In some embodiments, the embossed alphanumerical characters can be interpreted using computer implemented optical character recognition (OCR) to obtain true denomination of the coin. Additionally, a spectral analysis of the digital image of the coin can be performed to generate further discriminating aspects of the coins. For example, spectral analysis can be performed along different areas of the coin (e.g., at a given distance from a center of the coin). The obtained spectral peak can be compared to tabulated spectral values for the relevant coins in the market. Since a rectangular domain is generally better suited for spectral analysis than a round domain, the digital image of a round coin can be first mapped into a rectangular domain using, for example, a log-polar transform. The outline edge of the coin can be detected using line detection algorithms including, for example, Canny edge detection. Once the outline of the coin is determined, the diameter and the width of the coin can be calculated and used to discriminate the coins. Based on the discrimination results, the coin can be properly credited or rejected by the consumer-operated kiosk.
The following disclosure describes various embodiments of coin counting systems and associated methods of manufacture and use. Certain details are set forth in the following description and
In operation of this embodiment, a user places a batch of coins, typically of a plurality of denominations (and potentially accompanied by dirt or other non-coin objects and/or foreign or otherwise non-acceptable coins) in the input tray 102. The user is prompted by instructions on the display screen 112 to push a button indicating that the user wishes to have the batch of coins discriminated. An input gate (not shown) opens and a signal prompts the user to begin feeding coins into the machine by lifting the handle 113 to pivot the tray 102, and/or manually feeding coins through the opening 115. Instructions on the screen 112 may be used to tell the user to continue or discontinue feeding coins, to relay the status of the machine 100, the amount of coins counted thus far, and/or to provide encouragement, advertising, or other messages.
One or more chutes (not shown) direct the deposited coins and/or foreign objects from the tray 102 to the trommel 140. The trommel 140 in the depicted embodiment is a rotatably mounted container having a perforated-wall. A motor (not shown) rotates the trommel 140 about its longitudinal axis. As the trommel rotates, one or more vanes protruding into the interior of the trommel 140 assist in moving the coins in a direction towards an output region. An output chute (not shown) directs the (at least partially) cleaned coins exiting the trommel 140 toward the coin hopper 144. Trajectory of the coins through coin tubes 154a-b and return chute 156 is described in more detail with reference to
The illustrated embodiment of the coin counting portion 142 further includes a coin pickup assembly 241 having a rotating disk 237 disposed in the hopper 266 and a plurality of paddles 234a-234d. The coin rail 248 extends outwardly from the disk 237, past a sensor assembly having a source of light 274 and a detector 270, a digital camera 272, and further toward a chute inlet 229. A bypass chute 220 includes a deflector plane 222 configured to deliver oversized coins to the return chute 256. A diverting door 252 is disposed proximate the chute entrance 229 and is configured to selectively direct discriminated coins toward coin tubes 254a-b. A flapper 230 is operable between a first position 232a and a second position 232b to selectively direct coins to the first delivery tube 254a or the second delivery tube 254b, respectively.
In operation of the coin counting portion 142, the rotating disk 237 rotates in the direction of arrow 235, causing the paddles 234 to lift individual coins 236 from the hopper 266 and place them on the rail 248. The angle A encourages coins 236 to lay flat against the rail, such that the face of a given coin is generally parallel with the base plate 203. The coins 236 travel along the rail and pass the digital camera 272. Coins that are larger than a preselected size parameter (e.g., a certain diameter) are directed to the deflector plane 222, into a trough 224, and then to the return chute 256. Coins within the acceptable size parameters pass through the digital image acquisition system described below with reference to
The majority of undesirable foreign objects (dirt, non-coin objects, etc.) are separated from the coin counting process by the trommel 140 or the deflector plane 222. However, coins or foreign objects of similar characteristics to desired coins are not separated and can pass through the coin sensor (described below with reference to
In some embodiments, the digital image 402 can be pre-processed by artificially introducing a broad band noise (i.e., a Gaussian noise) to the image which, in turn, reduces the occurrence of the false-positive edge detections. The detected edges can be represented in a binary image, for example the image 402, where each pixel in the image has an intensity of either an edge pixel (e.g., high) or a non-edge pixel (e.g., low). Therefore, the detected edges can be represented as lines having high pixel intensity against a background at low pixel intensity. Various suitable computer programs that perform Canny edge detection methods are available in the public domain. For example, cv::Canny algorithm in the OpenCV computer vision library can be used. Once the edges on the coin surface are determined using a suitable edge detection algorithm, different aspects of the coin can be located more precisely.
For various coin denominations, the dots along the coin edge, lettering, numbering, and images (see, e.g., 411, 412, 413 and 414 respectively;
where x and y are the locations of the pixels relative to the center of the coin in the digital image shown in
Furthermore, locations of and distance among coin aspects and features can be determined using the transformed image 500 in
Additionally, the overall richness of the aspects of the coin image 500 in
The line image of the coin 802 shown in the side view 891 shows two groups of serration lines 860 that are separated by a distance L. In some embodiments, the number of the serration lines can be determined by a computer and used as an aspect to discriminate the coin. Furthermore, a diameter D and a thickness T can also be used as aspects to discriminate the coin. In some embodiments, the coin aspects can be combined and a voting scheme can be established to discriminate the coin against known coins in the market.
From the foregoing, it will be appreciated that specific embodiments of the invention have been described herein for purposes of illustration, but that various modifications may be made without deviating from the spirit and scope of the various embodiments of the invention. Many of the embodiments of the invention can be implemented using, inter alia, a general purpose digital computer having a processor or an industrial controller having a processor. Additionally, the methods explained with reference to
Claims
1. A computer-implemented method for identifying coins, the method comprising:
- obtaining an image of a coin with a camera;
- identifying at least one flat area in the image with a processor; and
- discriminating the coin with the processor by comparing the at least one flat area to a stored property of a known coin.
2. The method of claim 1 wherein the image is a first digital image, the method further comprising:
- generating a second digital image from the first digital image, wherein the second digital image is a line image, and wherein identifying at least one flat area is performed at least partially on the second digital image.
3. The method of claim 2 wherein identifying at least one flat area is based on detecting at least one portion of the second digital image that is free of lines.
4. The method of claim 2, further comprising:
- converting the first digital image of the coin to a gray scale image.
5. The method of claim 1 wherein the digital image is obtained in response to a signal generated by a coin movement.
6. The method of claim 1 wherein the image is obtained by a digital camera running at a selected frame rate.
7. A computer-implemented method for identifying coins, the method comprising:
- obtaining a digital image of a coin with a camera;
- performing a routine with a processor to identify a string of alphanumerical characters in the image;
- performing Optical Character Recognition (OCR) with the processor to recogne the string; and
- discriminating the coin by comparing the string of alphanumerical character to a stored property of a coin.
8. The method of claim 7 wherein the image is a first digital image, the method further comprising:
- generating a second digital image from the first digital image, wherein the second digital image is a log-polar mapping of the first digital image, and wherein identifying a string of alphanumerical characters is performed at least partially on the second digital image.
9. The method of claim 7, further comprising rotating the digital image.
10. The method of claim 7, further comprising
- measuring a distance between at least two aspects in the digital image.
11. The method of claim 7 wherein the string is a first string, the method further comprising:
- identifying a second string of alphanumerical characters in the digital image; and
- recognizing the second string based on performing the OCR.
12. The method of claim 7, further comprising:
- performing a routine to identify a diameter or a thickness of the coin.
13. The method of claim 8, further comprising:
- performing a Fourier transform of the second digital image to generate a transformed image; and
- discriminating the coin by comparing at least one aspect of the transformed image to a stored property of a coin.
14. The method of claim 13 wherein the at least one aspect is intensity of a peak in the transformed image.
15. The method of claim 13 wherein the at least one aspect is location of a peak in the transformed image.
16. The method of claim 7, further comprising:
- determining a first color in a first region of the digital image;
- determining a second color in a second region of the digital image;
- discriminating the coin by comparing at least one of the first and second colors to a stored property of a coin.
17. The method of claim 16 wherein determining a first color and determining a second color is based on pixel value thresholding.
18. A computer-implemented method for identifying coins, the method comprising:
- obtaining a first digital image of a coin with a camera;
- generating a second image from the first digital image, wherein the second digital image is a line image;
- identifying at least one edge in the second digital image;
- adding an image of a line to the second digital image;
- determining an angle between the at least one edge and the image of the line; and
- discriminating the coin by comparing the angle to a stored property of a coin.
19. The method of claim 16 wherein the image of the line and the at least one edge intersect.
20. The method of claim 16, further comprising:
- rotating the second digital image.
21. The method of claim 16, further comprising:
- detecting at least two aspects in the second digital image;
- calculating a distance between the at least two aspects; and
- discriminating the coin by comparing the distance to a stored property of a coin.
22. The method of claim 18, further comprising:
- selecting a plurality of aspects in the second digital image;
- counting a number of aspects; and
- discriminating the coin by comparing the number to a stored property of a coin.
23. A consumer operated coin counting system comprising:
- a coin input region configured to receive a plurality of coins;
- a digital camera configured to capture first digital images of individual coins;
- means for identifying at least one flat area in the first digital images; and
- means for discriminating the coin by comparing the at least one flat area to a stored property of a coin.
24. The system of claim 23, further comprising:
- means for generating second digital images from the first digital images, wherein the second digital images are line images, and wherein identifying at least one flat area is based on detecting at least one portion of the second digital images that is free of lines.
25. The system of claim 24, further comprising:
- means for generating third digital images from the second digital images using a log-polar mapping;
- means for applying a Fourier-transform on the third digital images; and
- means for discriminating the coin by comparing results of Fourier-transform to a stored property of a coin.
26. The system of claim 23, further comprising:
- means for converting individual first digital images to individual gray scale image.
27. The system of claim 23, further comprising:
- means for determining a diameter of the coin.
28. The system of claim 23, further comprising:
- means for identifying aspects in the first digital images;
- means for calculating distance between the aspects of the coin; and
- means for discriminating the coin by comparing the distance to a stored property of a coin.
29. A computer-readable medium whose contents cause a computer to discriminate coins, the coins being discriminated by a method comprising:
- obtaining a digital image of a coin;
- identifying a string of alphanumerical characters in the digital image;
- recognizing the string based on Optical Character Recognition (OCR); and
- discriminating the coin by comparing the string of alphanumerical character to a stored property of a coin.
30. The computer readable medium of claim 29, further comprising:
- accepting or rejecting the coin based on results of discriminating.
31. The computer readable medium of claim 29, further comprising:
- determining a first color in a first region of the digital image;
- determining a second color in a second region of the digital image;
- discriminating the coin by comparing at least one of the first and second colors to a stored property of a coin.
32. The computer readable medium of claim 31 wherein determining a first color and determining a second color is based on pixel value thresholding.
33. The computer readable medium of claim 29 wherein the digital image is a first digital image, further comprising:
- generating a second digital image from the first digital image, wherein the second digital image is a line image;
- identifying at least one edge in the second digital image;
- adding an image of a line to the second digital image;
- determining an angle between the at least one edge and the image of the line; and
- discriminating the coin by comparing the angle to a stored property of a coin.
34. The computer readable medium of claim 33 wherein the image of the line and the at least one edge intersect.
35. The computer readable medium of claim 33, further comprising:
- rotating the second digital image.
36. The computer readable medium of claim 29, further comprising:
- selecting a plurality of aspects in the digital image;
- calculating a distance between at least two aspects; and
- discriminating the coin by comparing the distance to a stored property of a coin.
37. The computer readable medium of claim 29, further comprising:
- selecting a plurality of aspects in the digital image;
- counting a number of aspects in the plurality of aspects; and
- discriminating the coin by comparing the number to a stored property of a coin.
Type: Application
Filed: Jan 17, 2014
Publication Date: Jul 23, 2015
Patent Grant number: 9443367
Inventor: Steven Baltazor (Black Diamond, WA)
Application Number: 14/158,514